This paper presents a role-adaptive Leader–Follower-based formation planning and control framework for teams of quadruped robots operating in cluttered environments. Unlike conventional methods with fixed leaders or rigid formation roles, the proposed approach integrates dynamic role assignment and partial goal planning, enabling flexible, collision-free navigation in complex scenarios. Formation stability and inter-robot safety are ensured through a virtual spring–damper system coupled with a novel obstacle avoidance layer that adaptively adjusts each agent’s velocity. A dynamic look-ahead reference generator further enhances flexibility, allowing temporary formation deformation to maneuver around obstacles while maintaining goal-directed motion. The Fast Marching Square (FM2) algorithm provides the global path for the leader and local paths for the followers as the planning backbone. The framework is validated through extensive simulations and real-world experiments with teams of quadruped robots. Results demonstrate smooth coordination, adaptive role switching, and robust formation maintenance in complex, unstructured environments. A video featuring the simulation and physical experiments along with their associated visualizations can be found at https://youtu.be/YFuAaYaGqd0.

This study investigates the effects of overburden stress on the physical and mechanical properties of stabilized fine-grained soil. Unconfined compressive strength (UCS) tests were conducted to evaluate the influence of overburden stress on stabilized soil specimens cured for 7, 14, 28, 60, and 90 days. The soil was treated with various binder contents of 1%, 2%, 4%, and 7% by soil dry weight. Two sets of stabilized soil specimens were prepared: one cured under atmospheric pressure and the other cured under stress of 10.5 kPa. The results indicate that the specimen density increased with both binder content and curing stress. The applied stress had the effect of increasing the unconfined compressive strength and stiffness, while a reduction in failure strain was observed compared to stabilized soil specimens cured without stress. The failure behavior is more brittle in stabilized soil with 7% binder content compared to soils with lower binder contents and natural soil, which exhibit more ductile behavior.

Commercial ferroalloys offer a resource-efficient alternative to high-purity elemental feedstocks for high-entropy alloy production, but impurity-derived inclusions require the systematic evaluation. Two CoCrFeMnNi high-entropy alloys were produced by high-frequency induction melting using high-purity elemental metals (Cantor-P) and commercial FeMn, FeCr, and FeNi ferroalloys supplemented with Ni and Co (Cantor-F). Inclusion chemistry, formation, and evolution were investigated using equilibrium and Scheil-Gulliver solidification calculations, scanning electron microscope equipped with energy dispersive X-ray spectroscopy (SEM-EDS), automated inclusion analysis, electrolytic extraction, and high-temperature confocal laser scanning microscopy (HT-CLSM). FeMn mainly contained MnO and MnO-SiO2 inclusions, FeCr contained Cr2O3 and Cr2O3-MnO inclusions, and FeNi contained abundant MnS, providing the potential precursors for complex inclusions in Cantor-F. The higher Si content in Cantor-F promoted SiO2-MnO-rich outer layers around Cr2O3-MnO based inclusions and increased the spheroidization of Cr2O3-MnO-Mn(S, Se)-SiO2 inclusions. The initial inclusion number densities were 617.1 and 673.4 mm-2, and the mean inclusion sizes were 1.73 and 1.81 μm in Cantor-P and Cantor-F alloys, and the sizes of most inclusions in both alloys were smaller than 2 μm. During HT-CLSM measurements, small Si-bearing inclusions in Cantor-F began to agglomerate at a separation of approximately 10 μm and exhibited a slightly higher apparent migration velocity. After remelting, the mean retained inclusion number densities were 45.3 mm−2 in Cantor-P and 32.4 mm−2 in Cantor-F. These results indicate a stronger apparent agglomeration and flotation tendency in Cantor-F, although the decrease cannot represent directly the measured inclusion removal efficiencies.

DDoS attacks continue to threaten Internet of Things (IoT) and Software Defined Networking (SDN) systems, where network traffic is highly dynamic and changes rapidly over time. Most machine learning and deep learning-based detection models assume stable traffic distributions and therefore struggle when attackers vary packet rates, flow intervals, and traffic intensities to avoid detection. Liquid Neural Networks (LNNs) offer a more suitable approach because they operate in continuous time and can adjust their internal states in response to time varying inputs. This study explores the use of LNNs for adaptive DDoS detection in IoT and SDN environments and evaluates their ability to model evolving and adversarial traffic patterns. The proposed framework uses three stacked liquid time constant layers combined with a multi head attention mechanism to learn both short term and long-term temporal dependencies in network flows. Three recent datasets, CICIoT2023, SNT SDN2025, and IoT23, were preprocessed through cleaning, normalization, and consistent feature scaling. The model was trained with the Adam optimizer and evaluated using accuracy, precision, recall, false positive rate, and false negative rate. Robustness was assessed using a multiple input multiple output adversarial testing approach based on the Momentum Iterative Method, which generates subtle perturbations to simulate real evasion attempts. The results show that the LNN based model achieves high performance on all datasets, with a maximum accuracy of 99.96% and significantly lower false positive and false negative rates compared with all baseline models including multilayer perceptron (MLP), LSTM, GRU, and CNN-LSTM. Under adversarial conditions, the LNN model retains more than 90% accuracy, while the strongest baseline CNN-LSTM drops to a maximum of 84.95%. These findings indicate that LNNs effectively capture the temporal variability present in DDoS traffic and generalize well across diverse attack scenarios. Overall, the results demonstrate that continuous time adaptive computation enables LNNs to provide robust, efficient, and temporally aware DDoS detection suitable for practical deployment in IoT and SDN environments.

This study investigates the tribological performance of chromium nitride (CrN)-coated polyether ether ketone (PEEK) polymers. CrN coating was deposited by physical vapor deposition (PVD) onto two commercially available substrates: neat PEEK and a modified PEEK. The suitability of the coated substrates for tribological applications was evaluated using ball-on-disc tests against stainless-steel (SS) counterparts. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) confirmed the high thermal stability of the PEEK substrates, ensuring compatibility with low-temperature coating deposition. Microstructural analysis revealed dense and continuous CrN coatings with an average thickness of ~1.5 µm on both substrates. Tribological results of selected PEEK-based polymers showed that under the applied load of 2 N, uncoated PEEK substrates exhibited lower coefficients of friction (COFs) and smoother wear tracks compared with coated samples. In contrast, under the applied load of 4 N, CrN-coated PEEK substrates demonstrated reduced friction and improved stability relative to uncoated PEEK. This behavior is attributed to load-induced tribo-oxidation and the formation of a chromium-oxide-rich tribolayer that stabilized the sliding interface and suppressed adhesive wear. Overall, the results demonstrate that CrN coatings significantly enhance the load-bearing capacity and tribological performance of selected PEEK substrates under applied load of 4 N, highlighting their potential for advanced lightweight engineering applications requiring improved wear resistance.

In this study, guided by a fit with the job and organization perspective, we examine how industry type, gender, and educational level are related to turnover among young managers aged 19–29. We also explore, based on the Job Demands-Resources framework, how young managers perceptions of various work environmental factors are associated with their intentions to leave their jobs. Register and survey data targeting all young managers (N = 2000) in the Swedish private sector were used to conduct comparative analysis between those who had left their position and those who remained. Among those who remained in a managerial position and responded to the survey (N = 395), we also examined the relative associations between a range of work environmental factors and turnover intentions. Overall, our results show that young female managers had a higher turnover rate than their male counterparts. Furthermore, emotional demands and job resources in terms of role clarity, perceived organizational support, and the ability to exert influence were associated with young managers’ turnover intentions. Our study provides new insights into why young managers may leave or remain in their position at an early stage. These findings can inform organizations and strategic human resource management practices, offering guidance on factors to consider for enhancing the retention of tomorrow’s managerial workforce.

Although trust is critical for entrepreneurs in all markets, it likely differs in gray markets from that in white or black markets. In this inductive study, we find a coherent configuration of trust mechanisms in a gray market that offers new insights into how entrepreneurs can use their network (i.e., referrals and “lineaging”) and heuristics (i.e., “villageing”) to build trust in new stakeholders when trust formation is challenging and violations are costly. We also offer new insights into the practices for verifying (i.e., impromptu tests and extensive probationary periods) and maintaining (i.e., financial support) trustworthiness, as well as repairing trust.

This paper is based on a systematic literature review, with the aim of identifying and discussing what previous research says regarding strategies local, regional and national actors have, to attract, recruit and retain residents and staff to the north of Sweden, Finland and Norway. Being geographically peripheral, grappling with a demographic challenge; with an aging and declining population, at the same time as there are increased work force needs due to the green transition and the changed military presence in the area, this is a clear case of a region with severe skills-supply issues.

A main finding is that the literature is rather limited, only 21 publications of relevance were identified. Three key themes emerged: attracting, recruiting and retaining staff (and residents) in the healthcare sector, immigration as strategy for attracting, recruiting, and retaining residents and staff, and regional and municipal development strategies. Rather few studies examine specific strategies, and even fewer their outcomes. One exception is research on the healthcare sector specifically, where it is found that connecting individuals as early as possible, via initiatives such as internship and decentralized education and training, along with offering good conditions for learning and professional development are key aspects in sustainable strategies for attracting, recruiting and retaining medical staff and residents to work and live in the rural north. Still, more studies are needed, on different levels, where the outcomes of specific strategies are examined. Such studies would contribute with much needed basis for policy makers in making evidence-based decisions regarding staff recruitment and retention.

The growing demand for large-scale geological hydrogen storage has driven the exploration of resource-efficient methods for repurposing existing underground infrastructure. This study assesses the technical feasibility of converting underground oil storage caverns into lined rock caverns (LRCs) for hydrogen storage and evaluates the material costs associated with two proposed design schemes. Two retrofit schemes were analyzed numerically using parameters derived from an existing LRC demonstration project. The ultimate and maximum operating gas pressures were determined using the uplift failure model, considering cavern burial depth and the mechanical properties of the surrounding rock mass. Structural responses under pressurization were evaluated, followed by a comparative assessment of concrete and steel liner costs. The results indicate that dividing the original cavern into multiple lined sub-caverns separated by concrete pillars improves structural performance and reduces the maximum tensile stress in the steel liner, although tensile-stress concentrations may develop in the concrete pillars. Meanwhile, the proposed Single-cavern scheme offers significantly higher hydrogen storage capacity with lower concrete requirements but a larger steel liner area. A supplementary short-term cyclic thermo-mechanical analysis revealed response-dependent differences from the corresponding static analysis. The static analysis overestimated the maximum tensile stress in the steel liner and the plastic-zone volume in the concrete liner, while underestimating the plastic-zone volumes in the rock-mass and total system. These findings highlight the importance of cyclic thermo-mechanical effects on LRC response while supporting the applicability of static analysis as a preliminary assessment approach, provided that an adequate safety margin is adopted when determining the maximum operating gas pressure. Additionally, in both schemes, the tensile-stress level in the steel liner remains low, suggesting significant potential for liner optimization in material selection and liner thickness. Overall, the results indicate that repurposing oil storage caverns as LRCs is a technically feasible retrofit option, while overall economic feasibility should be further evaluated through life-cycle cost analysis.

The transition towards safe, low-carbon, efficient energy storage and transportation is supported by the development of solid-state ammonia storage systems. Chemisorbents including alkaline earth metal halides (AEMHs), such as strontium chloride (SrCl2), have been studied for ammonia (NH3) sorption and delivery due to their high NH3 uptake. However, the volume expansion/shrinkage of AEMHs during sorption/desorption cycle significantly compromises their practical use. To improve structural stability, structuring additives (zeolites, graphite, metal-organic frameworks) have been studied, but often rely on nonrenewable components or complex fabication techniques, hindering scalability. In this study, microfibrillated cellulose (MFC) wa used as a structuring matrix for SrCl2, forming a highly porous composite called FCSr throuh a simple freeze-casting/drying with varying solid/water ratios. The resulting structure reched a porosity as high as 97% and was characterized by SEM, XMT, and FTIR. FCSr specimens could be loaded up to 90 wt% of SrCl2 salt resulting in a gravimetric NH3 uptake of 43.5 mmol.g−1, with mechanical and structural integrity and sorption kinetics 2.4 times faster than SrCl2 bulk of the same mass. In this study, sorption kinetics benefited from lower-density structures that promoted NH3 diffusion, though increased porosity reduced mechanical strength. A figure of merit analysis revealed that an optimal balance between sorption performance and structural integrity was achieved at 60 wt% of SrCl2 in the structured composite.

Ecohydraulics requires the integration of diverse skill sets to understand the complex interactions among hydraulic, biological, and ecological processes in aquatic environments, particularly under the influence of natural and anthropogenic change. This article presents findings from a community survey conducted at the 15th International Symposium on Ecohydraulics and Fish Passage (ISE-FP 2024) and compares them to those of a similar survey conducted at ISE 2018, with a particular focus on early-career researchers. We examine demographic trends, perceived challenges within the field, self-assessed competencies across general, technical, and emerging skills and identify training needs, seminar interests, and emerging technologies considered relevant to ecohydraulics. While demographic patterns and disciplinary challenges remain broadly consistent with previous findings, South American and African participation increased. Both early- and senior-career participants report confidence in traditional skills, such as literature review, fieldwork, and numerical modeling, but lower confidence in emerging technologies, including machine learning, uncrewed aerial vehicles, and artificial intelligence. Participants highlight the need for improved training in these technologies, interdisciplinary collaboration, and soft skills. These findings underscore the evolving needs of the ecohydraulics community and the importance of adapting education and professional development opportunities to support innovation, inclusivity, and cross-disciplinary engagement.

As climate change is resulting in more intense and more frequent extreme events of natural hazards, there is a need to study the vulnerability of infrastructure assets considering their condition and the relevant natural and man-made hazards. The GRDR methodology (abbreviation of Gestión de Riesgo de Desastres Regionales; Management of Risk of Regional Disasters) addresses this combination through the development of tools that can be implemented at the regional level. To develop the methodology, case studies have been used to gather feedback from professionals, relevant local performance indicators have been added, and methods to weigh and combine the observations have been developed. At this moment, the methodology has been applied to over 30 case studies in Chile, which are summarized in this article. In addition, the extensibility of the methodology is studied through a case study of the Tambura Bridge in Ecuador. By working towards unifying approaches to consider the effects of a variety of threats to structures in combination with their current condition, methods can be developed that may be adopted more broadly in Latin America.

The stability of the bottom structure and the scientific management of ore drawing are critical to the successful application of the block caving method. In view of the engineering conditions of the Lower Orebody of the Cukaru Peki Copper-Gold Mine operated by Serbia Zijin Copper Doo Bor characterized by great burial depth, high in-situ stress, and large-scale mechanized ore extraction. This study systematically optimizes the layout form and key geometric parameters of the bottom structure through a comprehensive approach combining engineering analogy, theoretical analysis, numerical simulation, and orthogonal experimental design. The results indicate that, in terms of the layout at the extraction level, the branching herringbone configuration is the optimal scheme. It not only ensures sufficient overlap of draw zones and stability of the sill pillar, but also better accommodates the efficient operation of electric load–haul–dump (LHD) machines. Regarding key parameter determination, coupled analyses of vertical and planar geometric relationships demonstrate that the optimal vertical distance between the extraction level and the undercut level is 20 m. The spacing of drawpoint crosscuts and drifts is determined to be 32 m and 18 m, respectively. The optimized dimensions of the ore pass are identified as 5.0 m for the lower opening width and 14 m for the upper opening width, which significantly reduce the incidence of drawpoint hang-ups. For the undercutting scheme, an advancing undercut method combined with an inclined narrow-section layout is recommended, with an undercut height of 6 m. Furthermore, numerical simulations reveal the influence of the advanced undercut distance on stress redistribution. It is recommended that a reasonable distance of 30–36 m be maintained between ore pass operations and the advancing undercut front to effectively mitigate structural damage caused by high stress concentrations in the bottom structure. The parameter system established in this study provides a scientific basis for the safe and efficient exploitation of the Cukaru Peki deposit operated by Serbia Zijin Copper Doo Bor and offers important engineering guidance for bottom structure design in similar deep block caving mines.

The densities and viscosities of the three amine-based deep eutectic solvents (DESs), i.e., [EmimCl][MEA], [BmimCl][MEA], and [HmimCl][MEA], mixed with ethanol (EtOH), were measured from 288.15 to 323.15 K, and their enthalpies of mixing were determined at 298.15 and 308.15 K. Negative excess molar volumes across the entire compositional range indicate significant volume contraction and enhanced molecular packing, and their temperature dependence suggests the dominance of packing effects over hydrogen-bonding. Viscosity deviations show a characteristic U-shaped composition dependence, reflecting disruption of the original DES hydrogen-bond network by EtOH. The enthalpy of mixing changes from positive to negative as the DES composition increases, revealing strengthened DES–EtOH interactions. The distinct composition-dependent trends observed for these three (DES + EtOH) systems indicate that variations in alkyl chain length modulate molecular packing and interaction strength, resulting in nonuniform ordering patterns across thermophysical properties. Compared to water, which strongly restructures the DES hydrogen-bond network and produces significant exothermic mixing effects, EtOH exerts a milder structural and thermodynamic impact. This work provides reliable thermodynamic data and mechanistic insight for the design and application of amine-based DESs.

Electrocatalytic nitrogen reduction is a vital strategy for green ammonia production. However, this is highly challenged by poor selectivity, and hence, the development of novel catalysts is scanty. The present research provides insight into the development of a new trimetallic alloy, CuAgMo, on multiwalled carbon nanotubes (MWCNT) for the nitrogen reduction reaction (NRR). Based on characterization techniques, the XRD shows successful growth of the trimetallic alloy on the MWCNT surface, and SEM–HRTEM images present the growth of CuAgMo nanoclusters on MWCNT and the existence of an interface. X-ray Absorption Spectroscopy (XAS) analysis confirms the reduction of metals toward cluster formation that could act as an active site for nitrogen reduction. The resulting CuAgMo@CNT nanocluster exhibits high activity and selectivity, achieving a Faradaic efficiency of 38.8% at −1.5 V versus the reversible hydrogen electrode in a 0.5 M KOH electrolyte. Furthermore, chronoamperometry shows that the electrocatalyst demonstrated stability for 90 h, outperforming most recently reported electrocatalysts for NRR. The outcomes of the present research demonstrate a feasible pathway for the rational design of electrocatalysts for N2 fixation.

Carcass grading is a process used to classify meat based on the quality and the yield, focusing on maturity, marbling, fat thickness, and muscling. In the EU, the EUROP system originally designed for beef is used [1]. This system has been automated using Video Image Analysis (VIA). While significant development has been made for beef grading, this is not the case for reindeer meat, due to the smaller scale and limited geographical distribution of production. However, this industry is very important in Nordic countries, due to its cultural significance and economic value for indigenous communities. 

Currently, reindeer carcass grading adopts the EUROP system and is performed manually by a reindeer meat inspector, which is errorprone. In this study, the development of an automated reindeer-specific carcass grading system is investigated using both classical and contemporary methods, including an AI-based data pipeline. Hyperspectral imaging (HSI) is explored for this purpose due to its potential [2]. HSI data of reindeer carcasses are acquired and analyzed. Camera parameters, lighting Fig. 1. Reindeer dressed carcass images in RGB (L), HSI (M) and HSI-osavi (R) formats. conditions, and carcass positioning are systematically evaluated to obtain HSI data suitable for grading purposes. The results are discussed, and recommendations are provided to enable effective reindeer carcass grading using this technology.

Vertical roller mills (VRMs) dominate cement finish grinding because of their high efficiency and dry operation. However, the interaction among ventilation, fan operation, and pneumatic material transport remains poorly quantified using plant data. In this study, the previously established datadriven Conscious Lab (CL) framework, based on explainable machine learning, is developed to model material transport behavior in an industrial VRM circuit at the Tehran Cement Plant. A total of 1050 operating records collected over eight months under diverse conditions were cleaned and analyzed. Three ensemble algorithms (CatBoost, XGBoost, and Random Forest) were trained to predict two transport-relevant targets, mill fan speed and mill fan power, and were assessed with conventional hold-out splits and nested cross-validation to reduce selection bias. Among the evaluated models, CatBoost delivered the most reliable performance, achieving high predictive accuracy for fan speed (R2∼0.97) and fan power (R2∼0.95). Interpretability was ensured through SHAP analysis, which ranked the dominant drivers of each target and revealed nonlinear interactions among differential pressure, grinding pressures, water injection, and feed-related responses. The results highlight the important role of ventilation-related variables, particularly mill fan speed, fan power, and differential pressure, in governing pneumatic transport and circuit stability. The proposed CL framework provides an explainable, operator-oriented tool to support process diagnosis, setpoint decisions, and future development of real-time intelligent control strategies for energy-efficient cement grinding.

Purpose

This study investigated the contribution of morphological knowledge (MK) to decoding of multimorphemic words in Swedish children at an early stage of reading development.

Method

Participants were 299 children in Grade 2 (ages 8–9), including 185 with typical reading development (TRD) and 114 with reading difficulties (RD) (girls n = 156). Children were recruited from 12 schools varying in socioeconomic status and included both first-language (L1) and second-language (L2) learners of Swedish. Mixed-effects linear regression models with random intercepts for school examined the contribution of MK while controlling for working memory, rapid automatized naming, nonword repetition, and general word reading skills.

Results

For the full sample and TRD group, MK uniquely and significantly predicted decoding of multimorphemic words. Specifically, the ability to manipulate the morphological structure of words was a significant predictor. For children with RD, MK was not a significant predictor, whereas working memory significantly predicted decoding in addition to word reading skills.

Conclusion

These findings suggest that MK plays a significant role in decoding in Swedish – a semi-transparent orthography with rich morphology – particularly after children have mastered basic grapheme–phoneme correspondences.

Background: 

Chronic Pain (CP) affects approximately one in five adults and is associated with emotional distress, disability, reduced productivity and substantial healthcare and societal costs. Acceptance and Commitment Therapy (ACT) has demonstrated effectiveness in improving emotional, behavioral and functional outcomes in individuals with CP, but access to ACT is frequently limited by geographic, organizational and workforce barriers. Internet-based interventions may help overcome these limitations by increasing accessibility, reducing treatment costs and improving scalability.

Objective: 

This study evaluates the effectiveness and cost-effectiveness of MobACT, a guided internet-based ACT programme for Italian adults with CP. The primary effectiveness endpoint is the post-intervention assessment (T1).

Methods and analysis: 

This two-arm randomized controlled trial will allocate adults with medically verified CP to either immediate MobACT or waitlist control (1:1). Participants in the waitlist control condition will receive access to MobACT only after completion of the T1 assessment, following the primary between-group comparison. A 6-month follow-up (T2) will be conducted in the intervention group only to examine maintenance of treatment effects. The primary outcome will be pain acceptance, assessed with the Chronic Pain Acceptance Questionnaire. Pain intensity and pain interference will be key clinical secondary outcomes. Additional secondary outcomes include quality of life, sleep quality, central sensitization, pain catastrophizing, psychological flexibility, self-efficacy, coping, anxiety, and depressive symptoms. Weekly assessments of sleep quality and changes in pharmacological treatment will also be collected during the active intervention period. The economic evaluation will compare the two conditions from healthcare and societal perspectives using service utilization data, productivity indicators, and EQ-5D-3L–derived quality-adjusted life years.

Ethics and dissemination: 

Ethical approval was obtained from the Ethics Committee of Università Cattolica del Sacro Cuore, Milan (Approval No.: 146/24). Results will be disseminated through peer-reviewed publications, international conferences and stakeholder reports for patient organizations.

Introduction: The ability to move fingers independently is essential for skilled hand use and relies on both biomechanical constraints and neural control. This study analysed finger independence and associated brain activation during individuated finger movements using simultaneous recordings of 3D kinematics and functional MRI.

Method: Twenty-five right-handed persons (age 62.3 ± 8.2 years) performed flexion-extension movements with individual fingers repeated for each hand during fMRI acquisition. Finger independence was quantified using a 3D motion capture-based Individuation Index (II), and neural activity was analysed with whole-brain BOLD responses. Movement frequency was controlled for in the statistical models. Contrasts between individual fingers and the thumb were used to identify finger-specific brain control regions, followed by region-of-interest analyses of areas commonly activated by fingers with low II. Laterality indices assessed inter-finger differences in hemispheric activation patterns.

Results: II were significantly higher for thumbs (0.98 ± 0.01) and index fingers (0.95 ± 0.03). The middle, ring, and little fingers showed comparable, lower II across both hands, with associations to increased activation in motor-related regions such as contralateral postcentral gyrus, ipsilateral cerebellum and ipsilateral central operculum. Laterality indices decreased with decreasing finger individuation, with significant reductions for the ring and little fingers compared to the index finger in parietal and postcentral regions.

Conclusion: The findings support that lower finger individuation ability is associated with increased recruitment of bilateral sensorimotor and ipsilateral cerebellar regions, even though the underlying mechanisms remain unclear. The integration of kinematic recordings in motor fMRI paradigms may be particularly useful for studying populations with impaired motor control, where variability in movement performance may otherwise confound interpretation of neural activation patterns.

Despite recent advances, Handwritten Text Recognition (HTR) for Arabic-script languages still lags behind Latin-script HTR. Part of the problem is dataset quality. To help closing this gap, we propose a two-stage framework (CER-HV) for detecting label errors. Stage 1 (CER) is a Character-Error-Rate-based noise detector built on a Convolutional Recurrent Neural Network (CRNN) architecture. Stage 2 (HV) is the Human-In-The-Loop (HITL) Verification of noisy samples detected by the first stage. Applying the CER-HV framework on multiple Arabic-script datasets can identify samples with label errors including transcription, segmentation, orientation, and non-text content errors that can markedly affect HTR performance. These errors were identified by the first stage of the framework with up to 90% (top-50) precision.We also show that our CRNN achieves state-of-the-art performance across five of the six evaluated datasets, reaching 8.46% Character Error Rate (CER) on KHATT (Arabic), 8.22% on PHTI (Pashto), 10.59% on Ajami, and 10.11% on Muharaf (Arabic), all without any data cleaning. We establish a new baseline of 11.3% CER on the PHTD (Persian) dataset. Applying CER-HV improves evaluation CER by up to 1.8 percentage points after dataset cleaning and retraining. Although our experiments focus on documents written in an Arabic-script language, the framework is general and can be applied to other text recognition datasets.

This study investigates the effect of thermo-mechanical densification on the fire behaviour of wood using cone calorimetry. Scots pine wood was thermo-mechanically densified from an initial thickness of 20 mm to target thicknesses of 14 and 10 mm and compared with non-densified specimens of the same thickness as well as non-densified 20 mm samples. For non-densified specimens, the time to ignition (TTI) remained nearly constant (18–20 s) across the investigated thicknesses, indicating thermally thick behaviour. In contrast, delayed ignition was observed for the specimens densified to 10 mm (the higher densification ratio), with TTI increasing to 28 s. While the first peak heat release rate (pHRR) remained comparable across samples, densification increased the time to the second peak heat release rate by up to 85% and its magnitude by approximately 9% relative to non-densified specimens of the same thickness (10 mm). Densification was found to alter the combustion behaviour of wood by modifying the thermal inertia of the material and the characteristics of the resulting char layer, thereby affecting the transport and release of pyrolysis gases and producing distinct effects at different stages of combustion rather than an overall enhancement of fire performance.

Sustainable recovery of critical resources demands catalytic platforms that are efficient, scalable, and low-energy. Contact-electro-catalysis (CEC) provides a promising route, yet conventional powder catalysts suffer from poor dispersion and limited recyclability. Here we introduce a prestressing strategy that regulates molecular ordering and interface charge states, yielding prestressed polytetrafluoroethylene (Ps-PTFE) films. Prestressing aligns dipoles, establishes a built-in electric field, and lowers the barrier for interface charge transfer under ultrasound-assisted, thereby amplifying reactive oxygen species generation. Relative to PTFE powders, Ps-PTFE films boost hydroxyl and superoxide radical production by 243.44 % and 152.29 %, respectively, delivering a 9.6-fold enhancement in catalytic efficiency. This enables highly efficient silver leaching from retired photovoltaic cells, with leaching, reduction, and overall recovery efficiencies of 97.37 %, 98.51 %, and 95.94 %, a 19.93 % improvement over conventional approaches. By establishing an all-film-state CEC platform, this work advances a recyclable, low-carbon, and scalable pathway for critical metal recovery, moving towards the United Nations Sustainable Development Goals.

Swedish environmental practice exhibits a paradox where excavation and landfilling dominate per- and polyfluoroalkyl substances (PFAS) remediation despite evidence that in situ stabilisation achieves equivalent exposure risk reduction at substantially lower cost. This article examines whether legal uncertainty rather than technical or economic factors drives this pattern. Through traditional legal method, the analysis identifies structural ambiguities in the Swedish Environmental Code (SFS 1998:808) (SEC) that systematically favour technically inferior approaches. Two distinct gaps create legal uncertainty: Swedish law provides no clear criteria for distinguishing between preventive measures under Chapter 2 and remediation under Chapter 10 when stabilisation requires long-term active management. Additionally, Chapter 10 establishes no framework for determining when remediation obligations are fulfilled if contaminants remain in place under indefinite management while risks are adequately controlled. These gaps create asymmetric liability exposure where stabilisation achieves superior environmental outcomes but generates indefinite legal obligations, whereas excavation provides definitive liability termination despite relocating rather than managing contamination. The findings demonstrate that operators may rationally optimise individual liability management rather than environmental protection when legal frameworks cannot accommodate technical realities. Resolving this paradox does not require untested legal innovation. Swedish law's nuclear waste and mining liability structures demonstrate that indefinite management can be operationalised through financial assurance and graduated responsibility. While PFAS present a distinct challenge requiring tailored statutory design, these frameworks suggest structural principles for enabling regulatory closure without assuming complete remediation.

Introduction: Curve squeal is a loud tonal noise from railway traffic that contributes to environmental noise pollution. Squealing arises from friction-induced vibrations in the wheel-rail contact during vehicle curving under specific friction conditions. Environmental factors are known to influence these friction conditions and, consequently, the tendency of squeal occurrence. This study investigates whether environmental variables measured from the wayside of the track can be used to predict curve squeal using machine learning models.

Methods: Separate prediction models were developed for low-rail and high-rail squeal using a labeled dataset collected during a 19-month measurement campaign at a commuter railway curve in Sweden. Multiple machine learning algorithms were evaluated, and feature importance analysis was used to investigate the environmental predictors associated with low-rail and high-rail squeal. The practical usefulness of the prediction models was further assessed for adaptive top-of-rail friction modifier (TOR-FM) application.

Results: Environmental variables contained useful predictive information regarding curve squeal occurrence, although the overall predictive performance remained limited. The tree-based ensemble models generally achieved the highest performance, while the feature importance analysis indicated that low-rail and high-rail squeal relied on different feature patterns. The operational assessment demonstrated that the prediction models could reduce unnecessary TOR-FM triggering while maintaining high squeal detection rates.

Discussion: The results demonstrate the potential of combining environmental monitoring with machine learning to support site-specific predictive railway noise mitigation and maintenance decision-making. The differences between low-rail and high-rail prediction further indicate the value of treating the two squeal types as separate prediction tasks.

Contact-electro-catalysis (CEC) converts mechanical excitation into interfacial redox chemistry, yet polar mineral water interfaces remain insufficiently engineered, limiting the efficient translation of spontaneous polarization into contact driven charge transfer. Here, we use surface hydroxylation to regulate the polar interface of natural tourmaline for enhanced CEC. Experiments and first principles calculations show that hydroxyl groups preferentially anchor at surface Al sites, forming a hydroxyl enriched interface while preserving the bulk crystal framework. This interface reorganizes the charge distribution, enhances effective interfacial polarization, raises the surface potential, and reduces charge transfer resistance, thereby facilitating H2O and O2 activation. Under ultrasound excitation, hydroxylated tourmaline produces ·OH and ·O2− signals 6.5 and 5.7 times higher, respectively, than those generated by spontaneous polarization alone. The enhanced CEC enables Rhodamine B degradation, heavy metal removal, and antibacterial activity. This work establishes hydroxylation regulated polar interfaces for mechanically driven environmental catalysis.

The behaviour of biomass in bioenergy and biorefinery processes is determined by its contents of cellulose, hemicellulose, and lignin. The conventional wet chemical methods used to determine these fractions are reliable but time-consuming and laborious. This study attempts to determine whether these fractions can be adequately predicted from derivative thermogravimetric (DTG) data alone. A total of 75 biomass samples, including bamboo, agricultural residues, shells, and binary blends, were used. In addition, we calculated 13 simple, physically meaningful descriptors from each DTG curve (67 points from 27 to 687 °C), including peak height and temperature, areas under fixed-temperature windows, and area ratios. We trained seven algorithms, one for each component: Random Forest, Gradient Boosting, Extreme Gradient Boosting, Ridge, Partial Least Squares, Support Vector Regression, and k-Nearest Neighbours. All models were evaluated using nested leave-one-out cross-validation, with parameters optimised in the loop, and model stability was assessed with repeated fivefold cross-validation. The engineered descriptors improved every component. Cellulose was predicted with moderate accuracy (cross-validated R2 of about 0.50 to 0.56, RMSE about 6.8%). Hemicellulose was weaker (R2 about 0.38- 0.43, RMSE about 4.8%). Lignin could not be predicted reliably (stable R2 about 0.13). The reason is physical: lignin decomposes slowly over a wide temperature range that overlaps with those of the other two components. SHAP and permutation analysis tied the predictions to sensible temperature regions. The study shows what DTG-based prediction can and cannot do across feedstocks, and it provides an open, reproducible pipeline.

Anode-less Li metal batteries suffer from rapid self-discharge after plating Li metal during charge. The speculated mechanism of self-discharge involves a combination of direct reactions between Li metal and electrolyte components as well as a Cu−Li galvanic pathway involving electrolyte reduction at the copper−electrolyte interface. However, direct separation of these pathways and quantification of their relative contributions as a function of the amount of plated lithium (plated Li capacity) in full cells has not been achieved. In this study, we combine 7Li operando NMR, electrochemical impedance spectroscopy (EIS), and scanning electron microscopy (SEM) imaging to quantify Li loss rates, track solid-electrolyte interphase (SEI) impedance growth, and visualize Li morphology evolution throughout self-discharge as a function of charge capacity. We find compelling evidence that self-discharge is initially dominated by rapid passivation of Li metal through direct Li-electrolyte reactions, with preferential loss of high surface area Li deposits observed within the first 2 h after charge. The initial rate of self-discharge is strongly dependent on the extent of Li plating, while long-term self-discharge rates are found to be relatively insensitive to plated-Li capacity. This suggests that on longer timescales, self-discharge kinetics are governed by galvanic corrosion. Self-discharge rates are accurately captured by a two-component kinetic model: a stretched exponential term describes the distributed kinetics of direct Li-electrolyte reactions (reflecting the range in Li morphologies), and a compressed exponential term describes the galvanic pathway (reflecting nucleation-propagation of Cu SEI on freshly exposed Cu surfaces).

This article contributes to research on educational governance by showing how accountability relations shape the interpretation and enactment of national policy at the meso level. By approaching accountability as a social relation rather than a policy instrument, it highlights how formally similar governance arrangements may give rise to different practices. The study examines Swedish school organizers, an understudied group occupying a central position in the governance chain, and how they navigate partly conflicting expectations from actors across governance levels. Drawing on the Accountability Regimes Framework, it explores (1) which expectations and demands organizers identify, (2) how they prioritize when demands collide, and (3) what governance practices emerge from these prioritization processes. The study uses abductive qualitative content analysis of semi-structured interviews with thirteen superintendents representing public, private for-profit, and private non-profit organizers in Norrbotten County, Sweden. The findings show that all organizers face extensive state steering often poorly adapted to local conditions, yet respond differently depending on organizational context. Public organizers rely on legality and administrative procedure, for-profit organizers emphasize internal alignment and efficiency, and non-profit organizers draw on proximity, values, and trust. Similar arrangements are thus enacted as distinct governance practices across organizational contexts.

Cocopeat is the most widely used substrate in soilless cultivation because of its high porosity and water retention capacity. However, its high import cost and limited availability highlight the need for sustainable, locally sourced alternatives. This study investigates the physicochemical, biological, and agronomic performance of a palm‑based substrate “palm‑peat” produced from date-palm residues and sugarcane bagasse as a potential substitute for imported cocopeat. The residues were processed through controlled washing, partial alkalization, and mechanical defibration to produce the final substrate. Comparative analyses with imported cocopeat included physicochemical properties (pH, electrical conductivity, bulk density and porosity), biological resistance (mold grown tests), and plant performance tests (germination rate, root–shoot growth, and leaf nutrient content). Each treatment was tested on 30 seedlings with three replicates in a randomized experimental design. Palm-peat exhibited physicochemical values within suitable horticultural ranges (pH 5.8–6.1; EC 1.2–1.4 dS m⁻¹; porosity> 85%), similar to those measured for imported cocopeat. Germination rates exceeded 80% in palm-peat based substrates. Root and shoot development were statistically similar to cocopeat (p < 0.05). Elemental analysis showed that palm-based substrates contained moderate levels of essential nutrients, including K, Ca, and Fe. Their physical performance was comparable to imported cocopeat. Processed palm-peat demonstrated strong resistance to fungal colonization and structural degradation. Overall, the results indicate that palm-peat can serve as an effective alternative to imported cocopeat under the tested conditions. Its utilization promotes circular bioeconomy by valorizing local agro-industrial residues while maintaining plant growth performance in horticultural systems.

This study investigates the early-stage incongruent dissolution behavior and carbon mineralization potential of mafic rocks representative of potential Swedish subsurface CO₂ storage reservoirs, including a dolerite, a gabbro, and a metabasalt. Batch experiments show that the dolerite exhibits the highest elemental release, followed by the gabbro and metabasalt. Dissolution in dolerite is primarily controlled by olivine and augite, with contributions from labradorite, whereas gabbro dissolution is dominated by augite followed by plagioclase. In the metabasalt, element release reflects contributions from multiple phases, including pyroxene, chlorite, prehnite, and oligoclase, without a single dominant phase. These patterns are consistent with incongruent dissolution during early water-rock interaction. Carbonation experiments indicate the formation of secondary carbon-bearing phases in all samples. Elevated Fe-rich precipitation suggests conditions favorable for Fe-carbonate formation, while thermogravimetric analysis supports the presence of hydrated Mg carbonates as well as Fe- and Ca-bearing carbonates. These results demonstrate favorable dissolution behavior and potential for carbon mineralization, supporting further evaluation of the investigated lithologies. Reactivity is primarily controlled by the availability of reactive primary minerals and the degree of alteration. However, successful implementation also depends on adequate knowledge of subsurface physical conditions, particularly fracture networks and permeability, which are critical for maintaining fluid circulation during injection of CO2 dissolved into the reservoir.

A novel short-process strategy for the synthesis of TiAl alloys was developed in this work, to achieve a potential cost reduction of 28.3 to 38.9%. Utilizing Mg ingots, Al ingots, and TiCl4 as the precursors, TiAl alloys with target compositions were prepared via a Mg-Al metal thermic reduction under the electromagnetic stirring condition. The obtained results indicate that the elemental distribution within both the TiAl alloy sponge and the cast ingots is relatively uniform. However, the TiAl alloy ingots exhibit a limited cold workability and high, non-uniformly distributed Brinell micro-hardness (HB). The average grain size is 136.37 μm, and there is a high proportion of high-angle grain boundaries (75.3%). Furthermore, the substantial local stress-strain concentrations are observed, accompanied by the complex lattice mismatches and defect structures, including the significant dislocations and twin structures. Finally, the mechanistic analysis of synthesis process based on Empirical Electron Theory (EET) and thermodynamic calculations aligns well with the experimental findings. This further validates the feasibility of this novel short process for advanced alloys manufacturing.

Coastal marshes are complex and biologically rich ecosystems supporting neighbouring assemblages of aquatic and semi-terrestrial species. They play a critical role in carbon sequestration (CS) and sediment trapping, thus operating as natural filters that improve water quality, supporting neighbouring ecosystems. However, coastal marshes are ecologically sensitive and historically have been threatened by varying factors including man-made infrastructures, global climate change, and meteorological events leading to severe marsh regression. This study presents a fine-grained identification of salt marshes of Venice Lagoon using multispectral Sentinel-2 satellite imagery. The lagoon of Venice is among the largest coastal wetlands in the world that has faced dramatic degradation throughout the last centuries due to the construction of the jetties. This necessitates the task of monitoring the long-term regression and accretion patterns of Venice for researchers to take ecological action against the regression of this large marsh ecosystem. This study presents a compelling case-study of two fine-grained marsh identification methods leveraging micro and macro-spatial context sizes, where micro-spatial inputs act as a magnifying tool for satellite imagery prioritizing entirely on spectral signature deviations, while macro-spatial inputs broaden the receptive context. We comprehensively evaluated the two marsh identification methodologies through holistic, granular, and Explainable Artificial Intelligence (XAI) analysis, thus addressing the black box nature of CNNs. We modified the architecture of various models by employing attention mechanisms resulting in greater performance metrics. We uncovered model-specific gradient-weighted spectral attention relative to the signatures of marsh bodies using Weighted Kernel Density Estimation (WKDE). We compared the correlation between model-specific Spectral Input Importance using Gradient-based techniques with Permutation Analysis by perturbing individual spectral inputs. Finally, we utilized the best performing model reported by our study to quantify decadal marsh and tidal dynamics by monitoring vegetation regression and accretion trends, seasonal marsh floodings, health indices, and chl-a concentration levels.

The rapid electrification of mechanical systems has intensified concerns about the effect of electrical potential on the tribological performance of polymer components used in electric drivetrains. Reciprocating ball-on-disk tribological tests were performed using a carbon-fiber-reinforced polyetheretherketone (PEEK CF30) disc sliding against an AISI 52100 steel ball under dry conditions while different DC voltages were applied across the contact. The results indicate that electrical potential affects the tribological behavior of the PEEK CF30/steel pair. In zero and low-to-moderate potentials (up to 10 V) the wear remains in the same range (10−7 mm3/Nm), while a potential of 20 V increases the wear by one order of magnitude to 6.76× 10−6 mm3/Nm. The friction also increased by 25%, achieving a value of 0.30 compared to the 0-V condition. However, at a high potential of 30 V, a softening effect becomes dominant, which in turn reduced the wear by three times to 2.08 × 10−6 mm3/Nm, accompanied by a friction decrease of 40%, reaching 0.19 relative to the 20-V condition. These findings demonstrate that electrical potential influences the tribological performance of polymeric composites reinforced with carbon fiber. The failure mechanisms in electrified tribological contacts are discussed for situations where the electric current acts as a tribo-chemical activation mechanism, the effects of which depend on the applied potential and the polymer composite's structure, promoting electrochemical reactions, Joule heating, accelerated wear, material transfer, and interfacial degradation.

Introduction: Patient-Controlled Admission (PCA) is an intervention aimed at increasing patient autonomy by allowing patients to admit themselves to psychiatric inpatient stays without physician assessment or staff gatekeeping. PCA is intended for patients with recurrent need of inpatient care, potentially those who would benefit from increased influence over the admission process. Studies suggest PCA may reduce involuntary care and improve patient empowerment. However, findings are difficult to interpret due to variation in implementation, lack of fidelity assessments, and the absence of an objective fidelity instrument.

Aim: To develop and evaluate a fidelity measure for PCA capturing core standards of fidelity across clinical settings while remaining acceptable and user-friendly in routine practice.

Methods: The development process of the fidelity instrument for chart review followed a five-step method: reviewing existing measures, analysing intervention components, drafting a fidelity checklist and guidelines, obtaining expert feedback, and piloting and refinement. Interrater reliability was assessed using Fleiss’ Kappa and percentage agreement, and acceptability and user-friendliness was evaluated in a routine clinical setting using Likert-scale questions.

Results: The Patient-Controlled Admission Fidelity Checklist (PCA-FC) consists of three parts: A1 (PCA as part of the care plan), A2 (the PCA-agreement), and B (PCA inpatient care). Item-level percentage agreement ranged from 90% to 100%, with several items demonstrating complete agreement. Fleiss’ Kappa coefficients ranged from non-estimable variables for items with no response variability to perfect agreement (κ =1.00). The PCA-FC demonstrated strong acceptability and usability indicating that the checklist was perceived as easy to understand, complete, and apply in practice. Fidelity scores aggregated at the organisational level showed variation, with the lowest adherence observed in inpatient care items.

Conclusion: The PCA-FC demonstrated high reliability for assessment of PCA fidelity through chart review. It may serve as a useful tool for implementation monitoring, clinical quality assurance, and research on effectiveness of PCA in psychiatric settings. However, fidelity is a multidimensional construct, and the PCA-FC represents an initial step in fidelity assessment that should be complemented by additional methods and further validation.

Air pollution has been recognised as the single biggest environmental threat to human health within the European Union (EU). Indoor air pollution has received less attention than outdoor air pollution, even though we spend most of our time indoors. It is associated with a variety of short- and long-term health effects. These include allergies and asthma with a key role for the exposure to bioaerosols and synergistic interactions between chemical and biological pollutants. Given the dramatic rise of allergic diseases, the prevention of symptom exacerbations and the new onset of disease are of great importance, particularly for vulnerable populations such as children. The lack of a unified EU-wide approach involving all stakeholders represents a major unmet need. The EU-funded IDEAL Cluster comprises seven research projects to advance knowledge on indoor air quality (IAQ) and its impact on health, inform policies and thus promote healthier indoor environments. The ‘SynAir-G’ project forms part of this cluster and was launched in 2022 with the participation of 18 partners from 11 countries. It focuses on schools to explore synergistic interactions between chemical and biological pollutants based on the hypothesis that these modulate major health effects. SynAir-G includes the development of low-cost sensors, collection of health outcomes, modelling of pollution dynamics and the development of mitigation solutions. The knowledge will be circulated to all stakeholders and the public aiming to establish standards and health-based guidelines. The scope of this position statement is (1) to review the current evidence on indoor air pollution, its impact on health and particularly on children with allergies and asthma; (2) to give an overview of the European regulatory framework; (3) to describe knowledge gaps, challenges and unmet needs; and (4) to present SynAir-G′s project goals and propose a roadmap to improve IAQ for children with asthma and allergies.

The space industry aims to promote economic growth, while ensuring a sustainable use of space. The sustainability objectives of space often refer to Active Debris Removal missions, which, despite being needed, are still following a linear economic model. Space circular economy permits giving value to space debris, considering it as a valuable resource. Pilot studies investigated the application of circular concepts to spacecraft solar panels. The results of a first study on the thermal degradation behavior of the solar cell encapsulant, WACKER® RTV-S 691, identified a degradation starting at 300 °C. It demonstrates great potential for further investigation of thermal delamination processes. This paper aims to propose an experimental approach to investigate the use of solar radiation to delaminate and to anneal spacecraft solar cells using a Sun simulator with a maximum power of 1500 Wm^-2. This initial study aims to start the discussion of the implementation of circular concepts within in-space assembly and manufacturing to facilitate their implementation.

Batteries are used in daily basis throughout our modern society. Solid-state and semi-solid electrolytes offer optimistic candidate for battery materials by replacing the conventionally used toxic and highly flammable liquid electrolytes. Gel polymer electrolytes (GPE) provide batteries a safe option and mechanical flexibility. GPEs can work without redundant use of rare earth metals like in brittle inorganic solid electrolytes. Ionic liquids and polar aprotic solvents are known to reduce polymer crystallinity, thereby enhancing ionic conductivity (IC) and ion diffusion in polymer electrolytes.

In this study, the addition of 1-methyl-1-propylpyrrolidinium 4,5-dicyano-2-(trifluoromethyl) imidazolate (MPPyrTDI) and various polar processing solvents was investigated using Daikin NEOFLON VT-475 fluoropolymer and poly (vinylidene fluoride-co-hexafluoropropylene), (PVDF-HFP). The resulting GPE membranes exhibited a highly amorphous and polar structure, facilitating efficient ion transport. The membranes demonstrated IC values exceeding 1 mS/cm. When implemented in lithium-ion batteries, the membranes showed stable electrochemical performance of 75 cycles with a capacity of 95 mAh/g, indicating good compatibility with electrode interfaces. These results demonstrate that the developed GPE membranes can serve as safe and efficient alternatives to conventional liquid electrolytes in lithium-ion batteries.

Malaria microscopy becomes clinically risky when parasitized erythrocytes are missed, even when a classifier reports high overall accuracy. This study presents an artificial-intelligence-assisted malaria microscopy framework that explicitly optimizes the diagnostic operating point for sensitivity-aware screening. A balanced set of 27,558 National Institutes of Health/National Library of Medicine (NIH/NLM) thin-smear cell images was divided by stratified image-level sampling in the ratio 70:15:15 for training, validation, and independent testing. Two custom convolutional neural networks (CNNs) with complementary capacity–regularization profiles and an equal-weight score-level ensemble were evaluated. Training-only online augmentation, fixed input resizing, validation-only threshold selection, confidence intervals, and error analysis were incorporated to improve reproducibility. At the conventional threshold, the compact CNN achieved the highest accuracy (95.26%). After optimizing the operating point with the recall-weighted F2 objective, the deeper CNN provided the strongest screening-oriented result on the held-out test set, reaching 97.05% sensitivity (95% confidence interval: 96.23%–97.70%), a 96.01% F2-score, a 2.95% false-negative rate, and a receiver-operating-characteristic area under the curve of 0.9876 (95% confidence interval: 0.9842–0.9910). It reduced missed parasitized cells from 177 to 61 relative to its default threshold. The public release does not provide patient identifiers; therefore, the reported split is image-level rather than confirmed to be patient-level independent, and external multicenter validation remains necessary before clinical deployment. The main finding is that the highest-accuracy classifier is not necessarily the most appropriate sensitivity-oriented screening configuration.

Chronic pain is both prevalent and debilitating. It is often treated with interdisciplinary pain rehabilitation programs (IPRPs). While research has largely focused on the effectiveness of IPRPs in improving patient outcomes, negative treatment effects remain under-explored. This study aims to investigate both the prevalence and types of negative outcomes following IPRPs for chronic pain. Data for the current study were obtained from the Swedish Quality Registry for Pain, with 22771 patients undergoing IPRPs between 2016 and 2023. We calculated the Reliable Change Index to find reliable deterioration across 18 patient-reported outcome measures, including pain intensity, physical function, psychological well-being, and vitality. Descriptive statistics were employed to determine the prevalence of deterioration, the prevalence of less severe deterioration, and the frequency of multiple deteriorations per patient. Results showed that while most participants did not report any areas of deterioration, about 23% of patients did experience reliable deterioration on at least one measure at the end of rehabilitation. Deterioration was most frequent in measures assessing physical status and pain, while psychological measures showed less deterioration. Mild deterioration on at least one measure was observed in 44.5% of patients. This study shows that a substantial proportion of patients experience at least some negative short-term outcomes following IPRPs, primarily in physical and pain-related outcomes. The findings underscore the importance of acknowledging and further investigating negative outcomes in chronic pain rehabilitation, and to take measures to address this issue.

In the rapidly advancing world of automotive technology, ensuring safety and reliability is paramount. A vehicle’s braking systemis crucial for preventing accidents and mechanical failures. Monitoring the braking system performance is vital, as any degradationcan have severe consequences. This study focuses on identifying key features to evaluate braking system performance. Vibrationsignals were analyzed under various conditions including air in the brake fuid, mechanical fade, reservoir leak, and diferent brakepad wear states. The feature extraction process used statistical, histogram, and autoregressive moving average (ARMA) techniques.These features were then selected using the J48 decision tree to determine their signifcance. Each feature type was independentlyclassifed to assess its efcacy, achieving classifcation accuracies of 98.00%, 99.00%, and 97.00% for statistical, histogram, andARMA features, respectively, using the random forest (RF) classifer. To improve accuracy, combinations of selected features weretested with histogram and statistical, ARMA and histogram, ARMA and statistical and histogram, and ARMA and statistical. Thehistogram and statistical features and the combination of all three features produced a classifcation accuracy of 100.00% with theRF classifer. Histogram and statistical feature combination was deemed most reliable, balancing high accuracy with reducedinformation and time complexity.

Malmberget is located in the northern Norrbotten ore province, Sweden, and hosts an iron oxide–apatite deposit of Kiruna type. The area is actively mined by the state-owned LKAB company, making it an electromagnetically noisy environment. New broadband magnetotelluric data have been collected and analysed, and the 3D electrical conductivity structure has been developed to improve the understanding of, and connection between, the local deposit and more regional structures. To mitigate the influence of noisy data on the inversion results, Huber’s loss function has been introduced and implemented based on an iteratively reweighted least squares scheme. The resulting robust 3D model shares major features such as the mine location and regional anomaly model acquired by standard L2 optimisation but shows differences on a local scale. Sites which received lower weights are generally identified as potentially noisy measurements due to the proximity to sources of anthropogenic noise, emphasising the ability of the scheme to quantitatively identify and deal with measurements contaminated by noise and improving overall data fit. Three main geological conclusions are gained from the 3D resistivity model. Firstly, a large east–west striking conductive anomaly at ~3−4 km depth is present in a large part of the model area. Secondly, three narrow, vertical, conductive structures connect the known mineralisation to the deep conductive anomaly. Thirdly, a second, deep conductive anomaly striking east–west in the south connects to the surface close to the Dundret ultramafic intrusion. Conductivity anomalies in the area are hypothesised to originate from a combination of magnetite mineralisation when close to the surface and iron-rich mafic rocks at depth, with possible presence of sulphides and/or graphite.

Rust is a modern systems level language with built in safety features providing strong guarantees to memory safety and defined behavior. In this work we identify challenges and opportunities for adopting Rust in the context of safety-critical systems. In particular we focus on software safety and security requirements akin to ISO 26262, IEC 61508, and ISO/SAE 21434. We discuss Rust based development in relation to commonplace C/C++ software design and validation processes. Furthermore we compare two distinct system categories: hosted and bare-metal systems and which opportunities and challenges developers face in the context of safety and security certification in each of them.

Critical raw materials (CRMs) are essential to the green and digital transitions, underpinning technologies such as renewable energy systems, electric vehicles, and advanced manufacturing. As demand increases, governments increasingly pursue policy objectives related to security of supply, diversification, sustainability, and innovation and circularity. While these objectives are often treated as distinct policy goals, their achievement depends on the institutional governance conditions through which CRM supply chains are organised. This article examines how institutional governance conditions shape the resilience and sustainability of CRM supply chains across different governance contexts. Drawing on Sustainable Supply Chain Management (SSCM) and the Triple Bottom Line (TBL), it employs a qualitative comparative legal and institutional analysis of Australia (lithium), the Democratic Republic of the Congo (cobalt), Canada (nickel), Chile (copper), and the European Union under the Critical Raw Materials Act (CRMA). The analysis demonstrates that legal and regulatory frameworks, administrative capacity and coordination, rights-based safeguards, and environmental governance operate collectively to shape resilient and sustainable CRM supply chains. The findings show that supply chain resilience depends less on resource endowment than on institutional governance conditions capable of providing regulatory predictability, procedural legitimacy, and long-term environmental stewardship. The study contributes by reframing security of supply, diversification, sustainability, and innovation and circularity as system-level governance outcomes rather than independent policy objectives.

Introduction: 

Radial distribution grids require renewable planning methods that jointly account for meteorological generation, WT/PV capacity allocation, and smart-inverter voltage support.

Methods: 

An ANN-PSO-VoltVAR framework was developed using 2012-2021 NASA POWER irradiance, wind-speed, and temperature data for In Salah, Algeria. Renewable uncertainty was represented by 12 stratified Weibull-Beta scenarios and an expected-cost/CVaR objective. The mixed decision vector optimized WT units, PV strings, and voltage-dependent inverter support at four preselected IEEE 85-bus locations. An exact backward/forward-sweep module on the standard 33-bus feeder independently verified the load-flow implementation.

Results: 

The tuned ANN achieved an RMSE of 1.6323 kW, an MAE of 1.2314 kW, and an R-squared value of 0.99999. The verified five-start mean-profile solution reduced mean active loss to 15.9900 kW and voltage deviation to 0.051082 p.u., while increasing minimum voltage to 0.98213 p.u. and minimum VSI to 0.96307. Relative to IFLO, the respective improvements were 54.57%, 22.49%, 1.21%, and 8.41%.

Discussion: 

The ablation analysis showed that capacity sizing accounted for most of the loss reduction, whereas Volt-VAR support produced the clearer additional voltage-security gains. Because the IEEE 85-bus electrical response is benchmark-calibrated rather than fully reconstructed, the findings should be interpreted as a reproducible proof-of-concept. Direct full-feeder validation on the IEEE 85-bus system and other networks remains necessary.

High-temperature drying can considerably improve drying efficiency. However, information on its impact on the mechanical properties of silver fir during industrial high-temperature drying remains limited. Elevated drying temperatures may induce changes in wood structure that can influence mechanical performance. This study evaluated the effect of high-temperature drying schedules based on industrially applied temperature levels (120 °C and 150 °C) on selected mechanical properties of silver fir (Abies alba Mill.). Wood samples were kiln-dried at 120 °C and 150 °C, conditioned to equilibrium moisture content, and tested for impact bending strength, tensile strength perpendicular to the grain, and Janka hardness. Because the experimental material originated from a single silver fir tree, the findings should be interpreted as describing responses within the investigated material rather than as population-level effects for silver fir. The results showed that drying at 150 °C was associated with a tendency toward reduced values of selected mechanical properties; however, the effect of drying temperature was not statistically significant for impact bending strength. ANOVA showed that specimen thickness significantly affected impact bending strength and Janka hardness, whereas drying temperature had a significant effect only on tensile strength perpendicular to the grain. The lowest mean values were observed in the β-150 group, with impact bending strength of 5.4 J·cm−2 and tensile strength perpendicular to the grain of 2.3 MPa. The mean oven-dry density was 456.9 kg·m−3, while only weak relationships between density and mechanical properties were observed (r2 = 0.023–0.178). This suggests that factors related to drying conditions may contribute to variability in mechanical performance beyond the effect of density alone. Thus, the effects of the investigated drying conditions were property-specific rather than consistently attributable to drying temperature. Within the investigated material, these results indicate that the optimization of high-temperature drying schedules should consider both drying parameters and variability in mechanical properties.

Addressing the inverse kinematics of multi-section continuum robots is challenging due to structural redundancy, which often results in conflicting training labels for data-driven models. This study proposes a novel discrete analytical framework that resolves this ”self-motion manifold” by establishing a bijective, deterministic mapping between end-effector positions and cable lengths. Using this geometrically consistent dataset (𝑁≈5000 samples), we evaluate and compare three learning-based architectures: Deep Neural Networks (DNN), Implicit Neural Representations (INP), and Gaussian Process Regression (GPR). To ensure a strictly fair comparative evaluation, all models were subjected to rigorous 5-fold cross-validation and equivalent hyperparameter tuning efforts, with the neural baselines evaluated over 30 independent runs to account for initialization stochasticity. The models were validated through both simulation and real-world experiments on a bionic continuum manipulator. Quantitative analysis reveals a distinct performance hierarchy: the GPR model achieves superior precision with the lowest Root Mean Square Error (RMSE: 0.0545–0.0600mm), significantly outperforming the DNN (RMSE: 0.0777–0.0849mm) and INP models (RMSE: 1.0095–1.3135mm). Furthermore, an analysis of practical metrics for real-time control demonstrates that GPR exhibits superior robustness to measurement noise by mathematically embedding continuous elastic mechanics. Crucially, in single-sample evaluations required for closed-loop control, GPR achieves an ultra-low inference latency of 0.2ms, matching the computational efficiency of the highly scalable DNN. These results confirm that GPR surpasses both DNN and INP in tracking fidelity and robustness without compromising real-time responsiveness, identifying it as the optimal strategy for enabling high-fidelity control of continuum robotic systems.

This paper evaluates how the design and resource use of an air-cooled data center-to-greenhouse energy symbiosis, operating in Swedish conditions, is affected by the desired microclimate criteria within the facilities. The necessary data center power, electricity use, heat reuse, water use, and air changes were evaluated by using analytical solutions and constrained, nonlinear optimization modeling of the heat and mass transfers in MATLAB. An analytical design expression is presented, which returns the minimum data center power per unit greenhouse area, as well as a fitted model to predict the energy reuse. By evaluating multiple design/operation cases for four locations in Sweden, the design data center power per unit greenhouse area was estimated as 0.40-10 kW·m−2, with an energy reuse factor index of 0.02-0.13 and a water use effectiveness of 5·10−5-0.22 L·kWh−1. Warmer climates together with proper microclimate matches (both in terms of temperature and specific humidity between the facilities) required substantially smaller data centers and produced significantly higher energy reuse factors. Poor matches between the data center and greenhouse microclimates could increase the greenhouse heat demand by over 1000 kWh·m−2·year−1 due to excessive need for humidification.

Growing demand for sustainable fuels and chemical feedstocks has intensified interest in waste- and biomass-derived oils as circular carbon resources. This study presents a techno-economic and GHG assessment of methanol production from three unconventional liquid feedstocks: waste lubricating oil (WLO), tire pyrolysis oil (TPO) and fast pyrolysis biomass oil (FPBO). Each feedstock is converted to synthesis gas via oxygen-blown entrained-flow gasification and subsequently processed through catalytic methanol synthesis. Process simulations were developed in Aspen Plus® and evaluated across plant capacities of 20–100 MW, under both conventional and hydrogen-assisted configurations. Carbon conversion to methanol increases substantially with hydrogen integration, rising from ∼22 to 40% in base configurations to above 85–90% in hydrogen-boosted cases. Methanol production costs for waste-oil pathways are estimated at 650–1100 € t−1, depending on feedstock and plant scale, and remain lower than those of CO2-based e-methanol (1200–1500 € t−1). GHG emission reductions of 42–133% (RCF) and 84–232% (BIO) relative to the fossil comparator are achievable when concentrated CO2 streams are captured or renewable hydrogen is introduced, while FPBO pathways can enable net-negative emissions. However, the current RFNBO framework under RED III favors carbon origin over energy allocation, potentially disadvantaging hybrid waste-carbon pathways that achieve superior exergetic performance through RFNBO compliant-H2 integration, despite meeting or exceeding the GHG reduction thresholds required for compliance.